https://github.com/bourrel/French-News-Clustering
FlauBERT: Unsupervised Language Model Pre-training for French
Papers with Code 아카이브
Word Sense Disambiguation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 16
- 보존된 평가 기록
- 181
The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet.. For example, given the word “mouse” and the following sentence:
“A mouse consists of an object held in one's hand, with one or more buttons.”
we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bplank/teaching-dl4nlp
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/cair/PyTsetlinMachineCUDA
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
https://github.com/cair/TsetlinMachine
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
https://github.com/cair/pyTsetlinMachine
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
https://github.com/cair/pyTsetlinMachineMT
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
https://github.com/cair/pyTsetlinMachineParallel
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/chandan047/MetaLearningForNER
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation
https://github.com/cheng18/bilm-tf
https://github.com/chrisociepa/allamo
https://github.com/ckinateder/pytsetlinmachineparallel
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
https://github.com/codedotal/gpt-code-clippy
https://github.com/conceptofmind/LaMDA-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/conceptofmind/PaLM-flax
https://github.com/conceptofmind/lamda-rlhf-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/contextlab/abstract2paper
https://github.com/crazydigger/Callibration-of-GPT
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/denizyuret/fastsubs-googlecode
https://github.com/dmlc/gluon-nlp
https://github.com/eternityyw/tram-benchmark
https://github.com/ethanjperez/true_few_shot
https://github.com/explosion/sense2vec
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings
https://github.com/facebookresearch/anli
https://github.com/facebookresearch/atlas
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/flairNLP/flair
https://github.com/foundation-model-stack/fms-fsdp
https://github.com/fywalter/label-bias
https://github.com/ggerganov/llama.cpp
https://github.com/ggml-org/llama.cpp
https://github.com/gmum/dl-mo-2021
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.